Ludger is a fourth-year PhD candidate at TU Munich with a MSc in Applied Mathematics from Imperial College London. His research interests lie at the intersection of scientific computing and machine learning with a focus on enabling uncertainty quantification and Bayesian analysis in large-scale fluid dynamics applications on high-performance computing systems. He assisted in the organization of the "Machine Learning for Physical Systems" symposium at the ECCOMAS World Congress of Computational Mechanics.
William is a final year Ph.D. Candidate at MIT, where he also received his M.Eng in electrical engineering and computer science (EECS) and B.S. in EECS and physics. His interests lie at the intersection of computer systems and machine learning, developing systems that automatically enable non-experts to leverage the latest in high-performance computing and ML. Within the field, he is known for developing Enzyme (NeurIPS '20), an automatic differentiation tool for LLVM capable of differentiating code in a variety of languages, after optimization, and for a variety of architectures (CPU and GPU). He has also worked on the Tensor Comprehensions framework for synthesizing high-performance GPU kernels of ML code, the Tapir compiler for parallel programs (best paper at PPoPP '17), and compilers that use machine learning to better optimize. He is a recipient of the U.S. Department of Energy Computational Science Graduate Fellowship and the Karl Taylor Compton Prize, MIT's highest student award.
Assefaw is an associate professor in the School of Electrical Engineering and Computer Science at Washington State University (WSU), where he leads the Scalable Algorithms for Data Science lab. His current research interests include: data science and AI, combinatorial scientific computing (CSC), high-performance computing, pervasive computing, and applications in energy systems, cybersecurity, and healthcare. In 2016, he received a National Science Foundation CAREER award for work on fast and scalable combinatorial algorithms for data analytics. His contribution to CSC includes algorithms and software tools for exploiting sparsity in derivative computation via automatic differentiation. He has served as co-chair of the SIAM Workshop on Combinatorial Scientific Computing in 2016, as mini-symposia organizer in numerous SIAM conferences, and routinely serves on program committees of major conferences. He recently (2020) organized a Virtual AI Mini-Summit at WSU that featured speakers from PNNL, Microsoft Research and WSU on wide-ranging topics in AI and applications in science and engineering.
Maria is a final year PhD student at the University of Edinburgh and a Machine Learning Research Engineer at Twitter Cortex. Her research interests encompass mainly probabilistic programming and improving Bayesian inference for probabilistic programs through program analysis. More generally, she is interested in tools for machine learning and the way they can empower future research. She has developed SlicStan, a self-optimising and more composable version of the popular probabilistic programming language Stan (POPL '19), and has also contributed to the automatic reparametrization procedure of the deep probabilistic programming language Edward2 (ICML '20).
Jan is an Assistant Computer Scientist at Argonne National Laboratory. He received his Ph.D. from Queen Mary University of London in 2017, and has been working on automatic differentiation (AD) for parallel architectures, formal software verification, performance engineering, and compiler technologies since then. He has organized or co-organized the annual workshop on AD, and a weekly seminar series at Argonne, and contributed to the development of multiple AD tools, including the popular Tapenade tool.
Sri is a Computer Scientist at Argonne National Laboratory. He conducts research in automatic differentiation, develops AD tools, and applies them to applications from different scientific domains. He has co-organized the EuroAD workshop in 2017 and 2020, a seminar series at Argonne, and co-organized a tutorial on AD at SIAM Conference on Computational Science and Engineering 2021.